Challenges Encountered During the Veterinary Disaster Response: An Example from Chile
Bibliographic record
Abstract
Large-scale disasters have immeasurable effects on human and animal communities. Evaluating and reporting on the response successes and difficulties encountered serves to improve existing preparedness documents and provide support to those in the process of developing plans. Although the majority of disasters occur in low and middle income nations, less than 1% of the disaster literature originates from these countries. This report describes a response to a disease outbreak in domestic dogs in Dichato, Chile following the 2010 earthquake/tsunami. With no national plan coordinating the companion animal response, there was a chaotic approach among animal welfare organizations towards rescue, diagnosis, treatment and record-keeping. Similar to the medical response following the 1985 earthquake near Santiago, we experienced problems within our own teams in maintenance of data integrity and protocol compliance. Loss of infrastructure added complications with transportation, communications and acquisition of supplies. Similar challenges likely occur in most disasters, but can be reduced through pro-active planning at national and local levels. There is sufficient information to support the human and animal welfare benefits of including companion animals in national planning, and lessons learned through this and other experiences can assist planners in the development of comprehensive and locally relevant contingency plans.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".